Agricultural systems exhibit a large degree of within-field yield variability. We require a better understanding of the drivers of this variability in order to optimally manage croplands. We investigated drivers of sub-field spatial variability in yield for three crops (hard red winter wheat, Triticum aestivum L. variety Langin; corn, Zea mays L.; and proso millet, Panicum milaceum L.) usings a multi-year dataset from a dryland research farm in northeastern Colorado, USA. The dataset spanned 18 2.6–4.3 ha management units, over 4 years, and included high-resolution topographic data, densely sampled soil properties, and on-site weather data. We modeled yield for each crop separately using random forest regression and evaluated model performance using spatially blocked cross-validation. The topographic position index (TPI) and increasing percent sand had a strong negative effect on yield, while the nitrogen application rate (N) and total soil carbon had strong positive effects on yield in both the wheat and millet models. Remarkably, TPI had almost as large of an effect size as N, and outperformed other more commonly used topographic predictors of yield such as the topographic wetness index (TWI), elevation, and slope. Despite the size and quality of our dataset, cross-validation results revealed that our models account for approximately one-quarter of the total yield variance, highlighting the need for continued research into drivers of spatial variability within fields.
